Low content cerium oxide nanoparticles on carbon for hydrogen peroxide electrosynthesis

A comparative study using different proportions of CeO2/C (4%, 9% and 13% CeO2) was performed to produce H2O2, a reagent used in the oxidation of organic pollutants and in electro-Fenton reactions for the production of the hydroxyl radical (OH center dot), a strong oxidant agent used in the electrochemical treatment of aqueous wastewater. The CeO2/C materials were prepared by a modified polymeric precursor method (PPM). X-ray diffraction analysis of the CeO2/C prepared by the PPM identified two phases. CeO2 and CeO2. The average size of the crystallites in these materials was close to 7 nm. The kinetics of the oxygen reduction reaction (ORR) were evaluated by the rotating ring-disk electrode technique. The results showed that the 4% CeO2/C prepared by the PPM was the best composite for the production of H2O2 in a 1 mol L-1 NaOH electrolyte solution. For this material, the number of electrons transferred and the H2O2 percentage efficiency were 3.1 and 44%, respectively. The ring-current of the 4% CeO2/C was higher than that of Vulcan carbon, the reference material for H2O2 production, which produced 41% H2O2 and transferred 3.1 electrons per molecule of oxygen. The overpotential for this reaction on the ceria-based catalyst was substantially lower (approximately 200 mV), demonstrating the higher catalytic performance of this material. Gas diffusion electrodes (GDE) containing the catalyst were used to evaluate the real amount of H2O2 produced during exhaustive electrolysis. The 4% CeO2/C GDE produced 871 mg L-1 of H2O2, whereas the Vulcan carbon GDE produced a maximum amount of only 407 mg L-1. Thus, the 4% CeO2/C electrocatalyst prepared by the PPM is a promising material for H2O2 electrogeneration in alkaline media. (C) 2011 Elsevier B.V. All rights reserved.

Numeric reconstruction of cytoskeleton with finite element method and topology optimization method

The importance of mechanical aspects related to cell activity and its environment is becoming more evident due to their influence in stem cell differentiation and in the development of diseases such as atherosclerosis. The mechanical tension homeostasis is related to normal tissue behavior and its lack may be related to the formation of cancer, which shows a higher mechanical tension. Due to the complexity of cellular activity, the application of simplified models may elucidate which factors are really essential and which have a marginal effect. The development of a systematic method to reconstruct the elements involved in the perception of mechanical aspects by the cell may accelerate substantially the validation of these models. This work proposes the development of a routine capable of reconstructing the topology of focal adhesions and the actomyosin portion of the cytoskeleton from the displacement field generated by the cell on a flexible substrate. Another way to think of this problem is to develop an algorithm to reconstruct the forces applied by the cell from the measurements of the substrate displacement, which would be characterized as an inverse problem. For these kind of problems, the Topology Optimization Method (TOM) is suitable to find a solution. TOM is consisted of an iterative application of an optimization method and an analysis method to obtain an optimal distribution of material in a fixed domain. One way to experimentally obtain the substrate displacement is through Traction Force Microscopy (TFM), which also provides the forces applied by the cell. Along with systematically generating the distributions of focal adhesion and actin-myosin for the validation of simplified models, the algorithm also represents a complementary and more phenomenological approach to TFM. As a first approximation, actin fibers and flexible substrate are represented through two-dimensional linear Finite Element Method. Actin contraction is modeled as an initial stress of the FEM elements. Focal adhesions connecting actin and substrate are represented by springs. The algorithm was applied to data obtained from experiments regarding cytoskeletal prestress and micropatterning, comparing the numerical results to the experimental ones

Combining fractal and deterministic walkers for texture analysis and classification

In this paper,we present a novel texture analysis method based on deterministic partially self-avoiding walks and fractal dimension theory. After finding the attractors of the image (set of pixels) using deterministic partially self-avoiding walks, they are dilated in direction to the whole image by adding pixels according to their relevance. The relevance of each pixel is calculated as the shortest path between the pixel and the pixels that belongs to the attractors. The proposed texture analysis method is demonstrated to outperform popular and state-of-the-art methods (e.g. Fourier descriptors, occurrence matrix, Gabor filter and local binary patterns) as well as deterministic tourist walk method and recent fractal methods using well-known texture image datasets.